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<p>Hi Hao,</p>
<p>the data covariance matrix is directly involved in the
computation of the beamformer weights, and therefore has an impact
on the output of the beamformer. So, you may want to make sure
that your covariance matrix is representative of the signal you
are interested in and also a good estimate (involving enough data
samples / a long enough time window).<br>
The beamformer weights are then applied to your original time
series, thus, your source signal has the same temporal resolution
as the original signal (note that the beamformer is a spatial
filter that defines the contributions of your channels to a given
source space position). <br>
</p>
<p>Cheers,<br>
Britta<br>
</p>
<p><br>
</p>
<br>
<div class="moz-cite-prefix">Am 06.07.2017 um 00:33 schrieb
jehherson chow:<br>
</div>
<blockquote type="cite"
cite="mid:5DD9A7B0-16A2-4E23-8492-CCB77FCA17B1@gmail.com">
<meta http-equiv="Content-Type" content="text/html; charset=utf-8">
<div class="">Dear Britta,</div>
<div class=""><br class="">
</div>
<div class="">Thank you for your answer. I am no expert into the
algorithms. To compute data covariance matrix, I guess the time
window is one dimension that could be used as the data samples,
but trial number could also play the role in the other
dimension. By select a time window, a common spatial filter
would be created that representing all the data within the time
window. My question is: will this kind of spatial filter make
the source output temporally smoothed or "affected"? (For it
collapses the data sample in the time dimension) Especially when
there are more than one sources in the time window of interest.</div>
<div class=""><br class="">
</div>
<div class="">Best,</div>
<div class="">Hao</div>
<br class="">
<div>
<blockquote type="cite" class="">
<div class="">On Jul 5, 2017, at 5:36 PM, Britta Westner <<a
href="mailto:britta.wstnr@gmail.com" class=""
moz-do-not-send="true">britta.wstnr@gmail.com</a>>
wrote:</div>
<br class="Apple-interchange-newline">
<div class="">
<meta http-equiv="Content-Type" content="text/html;
charset=utf-8" class="">
<div text="#000000" bgcolor="#FFFFFF" class="">
<p class="">Dear Hao,</p>
<p class="">the time window is needed to estimate the data
covariance matrix, one of the "ingredients" for
calculating the LCMV beamformer spatial filter that will
be applied to your sensor space data. Generally, the
estimate of this covariance matrix is better with more
data samples. Thus, spatial filters constructed on small
snippets of your data will be less reliable than spatial
filters constructed on a longer time window.<br class="">
Furthermore, if you use several time windows, i.e.,
several filters, I suspect that this can potentially
lead to discontinuities in your source time series (if
you intend to glue the output of your beamformers
together).<br class="">
Usually, to construct your data covariance matrix, you
would use a time window of interest, representing the
activity you are interested in. <br class="">
</p>
<p class="">I hope this helps,<br class="">
Cheers,<br class="">
Britta<br class="">
</p>
<p class=""><br class="">
</p>
<p class=""><br class="">
</p>
Am 05.07.2017 um 10:13 schrieb <a
class="moz-txt-link-abbreviated"
href="mailto:mne_analysis-request@nmr.mgh.harvard.edu"
moz-do-not-send="true">mne_analysis-request@nmr.mgh.harvard.edu</a>:<br
class="">
<br class="">
<blockquote type="cite"
cite="mid:CADeotZob+wQCReRG1kgOGuoo-Dq5Mabj-WUuY_+27m-bxidg0g@mail.gmail.com"
class="">
<pre class="" wrap="">From: jehherson chow <a class="moz-txt-link-rfc2396E" href="mailto:jehherson@gmail.com" moz-do-not-send="true"><jehherson@gmail.com></a>
Date: Wed, Jul 5, 2017 at 10:13 AM
Subject: [Mne_analysis] mne beamformer lcmv
To: <a class="moz-txt-link-rfc2396E" href="mailto:mne_analysis@nmr.mgh.harvard.edu" moz-do-not-send="true">"mne_analysis@nmr.mgh.harvard.edu"</a> <a class="moz-txt-link-rfc2396E" href="mailto:mne_analysis@nmr.mgh.harvard.edu" moz-do-not-send="true"><mne_analysis@nmr.mgh.harvard.edu></a>
Dear MNE Experts,
I am using mne lcmv to reconstruct the sources. I find in mne I can
apply lcmv beamformer on evoked data with a common spatial filter of
all experimental conditions and the entire time window (e.g. from 0ms
to 1000ms). The results look nice, but the method seems to be
skeptical. It seems that it’s better to use a moving window instead of
the entire window, which means that a moving window is kind of
preserving the temporal resolution, while the entire time window
sacrifice the temporal resolution. But the problematic thing of the
moving window method is that it requires more than one spatial filter
and the difference between these filters might make a confounding
output. Do you know which the right way to do beamforming?
Best,
Hao</pre>
</blockquote>
<br class="">
<br class="">
</div>
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